The Multiclass Score-Oriented Loss (MultiSOL) on the Simplex

• URL: http://arxiv.org/abs/2511.22587v1
• Date: Thu, 27 Nov 2025 16:20:55 GMT
• Title: The Multiclass Score-Oriented Loss (MultiSOL) on the Simplex
• Authors: Francesco Marchetti, Edoardo Legnaro, Sabrina Guastavino,
• Abstract summary: In supervised binary classification, score-oriented losses have been introduced with the aim of optimizing a chosen performance metric directly during the training phase.<n>In this paper, we use a recently introduced multidimensional threshold-based classification framework to extend such score-oriented losses to multiclass classification.<n>As also demonstrated by several classification experiments, this proposed family of losses is designed to preserve the main advantages observed in the binary setting.
• Score: 4.014524824655106
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the supervised binary classification setting, score-oriented losses have been introduced with the aim of optimizing a chosen performance metric directly during the training phase, thus avoiding \textit{a posteriori} threshold tuning. To do this, in their construction, the decision threshold is treated as a random variable provided with a certain \textit{a priori} distribution. In this paper, we use a recently introduced multidimensional threshold-based classification framework to extend such score-oriented losses to multiclass classification, defining the Multiclass Score-Oriented Loss (MultiSOL) functions. As also demonstrated by several classification experiments, this proposed family of losses is designed to preserve the main advantages observed in the binary setting, such as the direct optimization of the target metric and the robustness to class imbalance, achieving performance comparable to other state-of-the-art loss functions and providing new insights into the interaction between simplex geometry and score-oriented learning.

Related papers

• Multiclass threshold-based classification and model evaluation [4.014524824655106]
  We introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule.<n>Experiments show that multidimensional threshold tuning yields performance improvements across various networks and datasets.
  arXiv  Detail & Related papers  (2025-11-26T17:00:00Z)
• Cluster Purge Loss: Structuring Transformer Embeddings for Equivalent Mutants Detection [0.05461938536945722]
  We introduce a novel framework that integrates cross-entropy loss with a deep metric learning objective, termed Cluster Purge Loss.<n>We demonstrate state-of-the-art performance in the domain of equivalent mutant detection and produce a more interpretable embedding space.
  arXiv  Detail & Related papers  (2025-07-26T23:07:11Z)
• Multiclass threshold-based classification [2.66269503676104]
  We introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule.<n>We show that the multidimensional threshold-based setting yields consistent performance improvements across various networks and datasets.
  arXiv  Detail & Related papers  (2025-05-16T14:11:26Z)
• Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric [99.19559537966538]
  DML aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-03T13:44:20Z)
• Robust Class-Conditional Distribution Alignment for Partial Domain Adaptation [0.7892577704654171]
  Unwanted samples from private source categories in the learning objective of a partial domain adaptation setup can lead to negative transfer and reduce classification performance. Existing methods, such as re-weighting or aggregating target predictions, are vulnerable to this issue. Our proposed approach seeks to overcome these limitations by delving deeper than just the first-order moments to derive distinct and compact categorical distributions.
  arXiv  Detail & Related papers  (2023-10-18T15:49:46Z)
• Learning with Multiclass AUC: Theory and Algorithms [141.63211412386283]
  Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics.
  arXiv  Detail & Related papers  (2021-07-28T05:18:10Z)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)
• Learning by Minimizing the Sum of Ranked Range [58.24935359348289]
  We introduce the sum of ranked range (SoRR) as a general approach to form learning objectives. A ranked range is a consecutive sequence of sorted values of a set of real numbers. We explore two applications in machine learning of the minimization of the SoRR framework, namely the AoRR aggregate loss for binary classification and the TKML individual loss for multi-label/multi-class classification.
  arXiv  Detail & Related papers  (2020-10-05T01:58:32Z)
• Learning Gradient Boosted Multi-label Classification Rules [4.842945656927122]
  We propose an algorithm for learning multi-label classification rules that is able to minimize decomposable as well as non-decomposable loss functions. We analyze the abilities and limitations of our approach on synthetic data and evaluate its predictive performance on multi-label benchmarks.
  arXiv  Detail & Related papers  (2020-06-23T21:39:23Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Rethinking preventing class-collapsing in metric learning with margin-based losses [81.22825616879936]
  Metric learning seeks embeddings where visually similar instances are close and dissimilar instances are apart. margin-based losses tend to project all samples of a class onto a single point in the embedding space. We propose a simple modification to the embedding losses such that each sample selects its nearest same-class counterpart in a batch.
  arXiv  Detail & Related papers  (2020-06-09T09:59:25Z)
• Self-Supervised Tuning for Few-Shot Segmentation [82.32143982269892]
  Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. This paper presents an adaptive framework tuning, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme.
  arXiv  Detail & Related papers  (2020-04-12T03:53:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.